{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Desktop/Bike-Sharing-Dataset/day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  yr  mnth  holiday  weekday  workingday  weathersit      temp  \\\n",
       "0       1   0     1        0        6           0           2  0.344167   \n",
       "1       1   0     1        0        0           0           2  0.363478   \n",
       "2       1   0     1        0        1           1           1  0.196364   \n",
       "3       1   0     1        0        2           1           1  0.200000   \n",
       "4       1   0     1        0        3           1           1  0.226957   \n",
       "\n",
       "      atemp       hum  windspeed   cnt  \n",
       "0  0.363625  0.805833   0.160446   985  \n",
       "1  0.353739  0.696087   0.248539   801  \n",
       "2  0.189405  0.437273   0.248309  1349  \n",
       "3  0.212122  0.590435   0.160296  1562  \n",
       "4  0.229270  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#根据作业说明，去除casual,registered，且由于instant和dteday两个特征不能对目标y造成影响，也同时去除\n",
    "data = data.drop(['instant','dteday','casual','registered'],axis=1)\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           season          yr        mnth     holiday     weekday  workingday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     2.496580    0.500684    6.519836    0.028728    2.997264    0.683995   \n",
       "std      1.110807    0.500342    3.451913    0.167155    2.004787    0.465233   \n",
       "min      1.000000    0.000000    1.000000    0.000000    0.000000    0.000000   \n",
       "25%      2.000000    0.000000    4.000000    0.000000    1.000000    0.000000   \n",
       "50%      3.000000    1.000000    7.000000    0.000000    3.000000    1.000000   \n",
       "75%      3.000000    1.000000   10.000000    0.000000    5.000000    1.000000   \n",
       "max      4.000000    1.000000   12.000000    1.000000    6.000000    1.000000   \n",
       "\n",
       "       weathersit        temp       atemp         hum   windspeed          cnt  \n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000   731.000000  \n",
       "mean     1.395349    0.495385    0.474354    0.627894    0.190486  4504.348837  \n",
       "std      0.544894    0.183051    0.162961    0.142429    0.077498  1937.211452  \n",
       "min      1.000000    0.059130    0.079070    0.000000    0.022392    22.000000  \n",
       "25%      1.000000    0.337083    0.337842    0.520000    0.134950  3152.000000  \n",
       "50%      1.000000    0.498333    0.486733    0.626667    0.180975  4548.000000  \n",
       "75%      2.000000    0.655417    0.608602    0.730209    0.233214  5956.000000  \n",
       "max      3.000000    0.861667    0.840896    0.972500    0.507463  8714.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#观察特征的统计特性，主要观察的是数值型特征\n",
    "data.describe()\n",
    "#可以看到，cnt的标准差很大，所以在对cnt做处理的时候采用标准化，对其他数值型特征处理时采用归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25628950f98>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x256286f3320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察每个特征的分布\n",
    "#cnt散点图\n",
    "plt.scatter(range(data.shape[0]),data.cnt.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25628985a90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25628985668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values,bins=60,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(718, 12)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#去除离群点\n",
    "data = data[data.cnt<8000]\n",
    "data = data[data.cnt>50]\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25628d9a198>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25628a3b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#观察数值型特征的散点图分布\n",
    "plt.scatter(range(data.shape[0]),data.temp.values)\n",
    "plt.scatter(range(data.shape[0]),data.atemp.values)\n",
    "#观察到temp和atemp特征分布基本相同，可以舍弃其中一个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x25628df0198>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2562893bb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data.hum.values)\n",
    "plt.scatter(range(data.shape[0]),data.windspeed.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25628dff6d8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25628ddb588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#temp与atemp分布相似，于是只看一个特征的直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.temp.values,bins=60,kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FhonWmPsFZrbNzNaa2eIovaZIVK3b20xSwJgzRVMgo2VWUTbLpuXz6y31Xpciw0Qj3DcDM5xzy4D/Bp48WUMzu8vMKs2ssqWlJQpfWiRy6/a2cM70SaRrCmRUfXj5VHY3tLOnsd3rUmSIMw5351y7c64zfH8NkGJmhSdpe79zbqVzbmVRUdGZfmmRiDV39LCzvp1L5+vnLto+tGwqSQHjSfXeY8oZb2RtZiVAk3POmdl5hH5hHD3jykSi6LV9RwBYPb+IbTXjv13twxuqx/1rxIrC7DQumVvIU1vr+Oo18wkEdLI6FkQyFfIR4C1gvpnVmtmdZvYZM/tMuMktQJWZbQO+C9zmnHPjV7LI2K3b20xRThqLSrV/+3i4eUUZDW09rD+gfl2sGLXn7pz76CjP3wfcF7WKRKJsYNDx+jtHuGpRsaZAjpNrFpeQk57MY5U1XDhnxFFZmWBaoSq+t7XmOG3d/Vw2f4rXpfhWekoSNy8vY21VI21d/V6XIyjcJQG8sqeFpIBx8Vz1KMfTredOoy84yFOa8x4TFO7ie6/sbeac6ZPIy0jxuhRfW1KWx6LSXB7bVON1KYLCXXyuuT00BXL1Ak2BnAi3njuNnfXtuoB2DFC4i6+t2xdaLKfx9olx8/IyUpMDPLopcaaCxiqFu/jaur3NlOSms6BE+8lMhLzMFD50Vim/3lxHR49OrHpJ4S6+1R/eBXL1/CJNgZxAd1xQwYm+AX69RSdWvaRwF9/afPg4HT1BVmtIZkItn5bPWeV5PPTWYbSe0TtnvP2ASKx5b+n/b6oaCRhcNGeyxxUlhqFbLsybksPjm2t5692jv1/UNHxLhttXTZ/Q+hKNeu7iW/uaOqiYnEVOuqZATrSl5XlMykzhobcOe11KwlK4iy+1dfeHLoStE6meSEkKcOu503lhVyM1x7q8LichKdzFl/Y1dgC6ELaX7rhgBgEzfvK7Q16XkpAU7uJLe5s6yM9IYUqOLoTtlan5GXzorFIe3VhNW7emRU40hbv4TnBwkP0tncwr0YWwvfapD8ziRN8Aj27UoqaJpnAX3zl8tIu+4CDzNSTjqYc3VLO9to1ZRVl875X9BAcHvS4poSjcxXf2NnaQFDBmFWV5XYoAH5hTRHtPkB212m9mIincxXf2NHYwszCLtGRdCDsWzCvOpiQ3nXV7WxjUoqYJo3AXX3m3pZMjnb0s1BTImGFmXLZgCi2dvdotcgIp3MVXXtzVBMBCXSs1piyemsuUnDRe2dus3vsEUbiLr7y4q4mpeenkZ6Z6XYoMETDjsvlTaGrvZVd9u9flJASFu/hGS0cvm6uPq9ceo5aW51GYncore5u1odgEULiLb7y8pwnnNCQTqwJmrJ4/hYa2HvaEVxDL+FG4i2+8uKuJsvwMSvPSvS5FTmJZeT4FWam8vEe99/E2arib2YNm1mxmVSd53szsu2a238y2m9nZ0S9T5NS6+oK8/s4RrlpUrFWpMSwpYKyeV0Rdazevhi+BKOMjkv3cfwLcBzx0kuevA+aGb6uA74f/FRkXw/cFB9hV30ZvcJDACME+UnvxzvLp+by8p5nvvPQOl87TVbLGy6g9d+fca8CxUzS5CXjIhawH8s2sNFoFikRiV0MH6SkBZhZqVWqsSw4EuHR+EVuqW3ntnSNel+Nb0RhzLwNqhjyuDX/sfczsLjOrNLPKlhb9SSbRMegcexrbmV+cQ1JAvcB4cM6MSZRPyuDfn9/D4KDG3sdDNMJ9pHfTiN8t59z9zrmVzrmVRUVFUfjSIqGNwrr6BjRLJo4kBwJ86cp5VNW1s7aq0etyfCka4V4LTBvyuByoj8LrikRkd0M7SWa6MEecuXlFGfOKs/nPF/YSHNCOkdEWjXB/GrgjPGvmfKDNOdcQhdcVGZVzjt0N7cwqyiI9RRuFxZOkgPHXV8/nwJETPP52rdfl+M6os2XM7BFgNVBoZrXAPwApAM65HwBrgOuB/UAX8GfjVazIcM0dvRw90cdFcwq9LiXhRGMW0lWLilkxPZ/vvPQON68o0y/oKBo13J1zHx3leQfcHbWKRMZgd0NonxKNt8cnM+Mr18zn9gc28LO3DvPpS2Z5XZJvaIWqxLWqujamTcogLyPF61LkNF04u5APzC3k/63bT0ePrrUaLQp3iVtHO3upb+thSVme16XIGfrqNQs43tXPA68f9LoU31C4S9x678IPCvf4t7Q8j+uXlvCj1w/Q3NHjdTm+oHCXuLWjPjQkM0l7t/vCV65ZQF9wkG//9h0gdMJ26E3GRuEucenYiT7qWzUk4yczC7P42PkzeHRjNe80aUvgM6Vwl7i0Q0MyvvSFK+aSlZrMN9fu8bqUuKdwl7hUVddGuYZkfKcgK5W7L5/DS3uaebel0+ty4prCXeLOkY5e6lq7Wapeuy998sIKyvIzWLujQRfTPgMKd4k7W2tbMeCs8nyvS5FxkJ6SxFevnU99Ww/balq9LiduRXKxDpGY4ZxjW00rM4uytHApzp1qBsygc5TlZ/DCriaWlOWRkqR+6Fjpf0ziyrbaNo6e6GO5eu2+FjDjuqUltHX38+Z+XdDjdCjcJa48uaWO5IBplkwCmFWYzcLSXF7d10Jnb9DrcuKOwl3iRnBgkGe31zO/JEe7ByaIaxeX0D8wyMt7mrwuJe4o3CVuvPnuUY509rFMQzIJoygnjXMrCth48JimRo6RTqhK3PjFphomZaawoERXXEokVywsZmtNK3/x8BY+fv6M33/89lXTPawq9qnnLnHh2Ik+XtjVyM0rykjWzImEkp2WzKXzitjd0M4B9d4jpneJxIVfb6mjf8Bx67nTRm8svnPRnELyM1JYo4VNEVO4S8xzzvGLTTUsK89jQYmuuJSIUpICXLO4hPq2HrZUH/e6nLigcJeYt622jb1NHfyJeu0J7azyPKZNyuCFnU30Bge8LifmKdwl5j22qYb0lAA3LJvqdSniITPjg2dNpaM3yGv7WrwuJ+Yp3CWmtXX18+SWOm44ayq56dpuINFNL8jkrPI8Xn/nCHWt3V6XE9MU7hLTHqusprt/gE9eVOF1KRIjrl1cAsC//UZ7vp9KROFuZtea2V4z229mfzvC8580sxYz2xq+fSr6pUqiGRh0/PR3hzlvZgGLp2q7AQnJz0zl4rmFPLW1XidXT2HUcDezJOB7wHXAIuCjZrZohKaPOeeWh28/inKdkoBe3NVEXWs3/0e9dhnm0nlFFOWk8Y/P7sJpauSIIum5nwfsd84dcM71AY8CN41vWSLwP28epCw/gysXFntdisSYtOQkvnL1fDZXt/LM9gavy4lJkWw/UAbUDHlcC6waod0fm9klwD7gS865mhHaiERke20rGw4e42vXLeAXlbVelyMxqG9gkNK8dO55sorjJ/r4xIUVXpcUUyLpudsIHxv+d9AzQIVz7izgt8BPR3whs7vMrNLMKltaNJVJTu47v32HvIwU7R8iJxUw44NLS2nVnu8jiiTca4Ghq0fKgfqhDZxzR51zveGHDwDnjPRCzrn7nXMrnXMri4qKTqdeSQA7att4aU8zn7p4Jjma/iinMKsom0Wluazb20Jze4/X5cSUSMJ9EzDXzGaaWSpwG/D00AZmVjrk4Y3A7uiVKInmOy+Feu2f0IlUicB1S0oYcI5vamrkHxg13J1zQeAvgOcJhfYvnHM7zexeM7sx3OwLZrbTzLYBXwA+OV4Fi79V1bXx291N3HnxTC1akohMzk7jA3MKeWJzHRsPHvO6nJgR0X7uzrk1wJphH7tnyP2vAV+LbmmSaJxz/Mua3eRlpGjRkozJ6vlT2NfUwT1PVfHs5y/WttBohapMgIc3VP/B7WTWVjXyu3eP8uWr56nXLmOSmhzgnhsWsaexg5+tP+x1OTFB4S4xobtvgH9+bjcLSnK4/TzNkJGxu2ZxCZfMK+JbL+yjuUMnVxXuEhO+v24/da3dfP3GxfqTWk6LmfH1GxbRExzgm2t0clXvIvHczvo2fvDqAW5YNpXzZ032uhyJY7OKsrnrklk8sUUnVxXu4qnuvgG+8MgW8jNT+MaNi70uR3zg7svmUJafwT1PVREcGPS6HM9ENFtGZLz803O7eLflBD+78zwKslK9Lkfi2NCT9avnF/HzDdXc//oBPrd6jodVeUc9d/HMU1vr+PmGaj79gZl8YK5WLEv0LJ6ax3VLSvj2b99hf3OH1+V4QuEunlh/4Chf+eV2zptZwF9fM9/rcsSHvnHTYjJTk/jq49sZGEy8bYEV7jLh9jd3cNdDlUyfnMkDH19JWnKS1yWJD03JSecfbljE5upWHnzjoNflTDiFu0yoxvYePvrABlKTk/ifT55LXqYWK8n4uXl5GVcuLObfn9/Lzvo2r8uZUAp3mTA1x7p44LUDBAwe+fQqphVkel2S+JyZ8W+3nEV+ZgqffHATP3nz0Kgrpf1C4S4ToqqujR+/cZCM1CQe/8yFzC3O8bokSRAFWal860+W09LZy3M7EueqTQp3GVcDg47ndzby8MZqinPTuOuSWeqxy4S7eG4hl8wtZNOhY7x9ODEuqq157jJu6lq7+avHtrLh4DHOrSjghrNKtbWAeOaqRSXUtnbz1NY6puSkeV3OuNM7TaLOOcevt9Ry7bdfo6qujVvOLufDK8oU7OKppIDx0XOnk5OezM83HPb9lZv0bpOoOnz0BHc8uJEvPbaNecU5rP3iJZw9Y5LXZYkAkJWWzMfOn0FP/yB3PLiRtq5+r0saN+acN5P7V65c6SorKz352hJdD2+oJjg4yBvvHOHlPc0kBYyrF5ewamYBAXv/9dWHX/Q6EWYuSGzZ39zJ/64/zNLyPH5253lkpsbPCLWZve2cWzlaO/Xc5YwdaOnkvpf388KuJhaU5PClK+dxwazJIwa7SCyYMyWb79y2nC3Vx/n0Q5V09ga9LinqFO5y2vY1dXDnTzbxozcO0hcc5I7zZ3D7qhnkZmhhksS+65aW8u+3LGP9gWPc/sB6jnb2el1SVMXP3yISMxrbevjWi3t5/O1astKSuWZRMRfOKSRFJ0wlzvzxOeXkZaRw98ObueUHb3H/x8/xzRoMvRslYnWt3dz7zC5W/8crPLmlnj+7aCavfeUyLp0/RcEucevKRcX876dW0d7dz433vcmv3q71uqSoUM99jIaf/Bt+cjAWnUnNzjn++bndbDx0jKq60N4cH15Rzl9eOfe0FyPpBKrEmnMrCljzxQ/whUe28OVfbuPlPc383w8tpDQvw+vSTpvCXd7HOcfO+nZ+U9XIs9vrOXS0i/SUABfMmsxFcwr53GWJefED8bfi3HR+/qlVfH/du9z3yn5e2dvM51bP5o4LK8hNj7/zSBGFu5ldC3wHSAJ+5Jz75rDn04CHgHOAo8CtzrlD0S1Vxkt33wB7Gtupqm9n48FjrD9wlJaOXpICxqqZBZxbUcCSsjwNvYjvJScF+PwVc7l5RRn3PruL/3hhHz989QC3r5rOredOY1ZRttclRmzUcDezJOB7wFVALbDJzJ52zu0a0uxO4Lhzbo6Z3Qb8K3DreBQskXPO0d4dpLmjh87eIJ09QTp7g9S3dnOks5e61m4OHT1B3fFu3ruWwZScNC6cHeqhX7mwmIKsVA2jSMKZVpDJA3esZEdtGz987V0eeP0AP3ztAIun5nL1ohLOn1XAsmn5pKfE7rUIIum5nwfsd84dADCzR4GbgKHhfhPw9fD9x4H7zMzcBKyQcs4x6EIbVA06h3Mw6BwDzhEccPT0D9DTP0BvcPAP/u3pH6S7P0hX3wDd7936B+jqC7Xv6ht6P0j/gMMMjp3oA8CApECA53c2kpmaREZqEhkpSeH7yWSkJJGREiAjNYn0lNAtIyXULj05iYzUAGnJSSQnnXwu+Hv1d4frfe9YeoKDnOgN0t7dT1v41t4TDP0bvoU+1k//wPu/BQGDgqw0yvLTWTFtEn+0opxFU3NZVJpL+aQMTPPTRQBYWp7HfbefTWNbD8/taOCZbfV8+6V9uN9CSpIxqzCbOcXZlOdnUJidRmFOKoXZaUzOSiMz/N5PSw6QlhIgNSlAUsAm7P0VSbiXATVDHtcCq07WxjkXNLM2YDJwJBpFDrV2RwNfeHRLOMyj+9rJASMjNRz5pg3NAAAE3ElEQVTQKe+FdIDM1GRSkgwH9PQP4Bw4Qr9QWrv6qG/9w18K3f0D0S1slJpzM1LIy0ghNz2Z3IwUyidl/P5jk7NS2dfUQXZaCtlpyWSnJ3PnxTNJCijARSJVkpfOnRfP5M6LZ9La1cemQ8fZXH2cfY0d7Kht48WdTfQNDEb0Wmbw2Utn89VrF4xrzZGE+0gpMDxWI2mDmd0F3BV+2GlmeyP4+rGmkHH4pTWR/jz0z2kfx59GsZYoiPvvR5iOY4KN8nM8rsfxN9+Evzn9T58RSaNIwr0WmDbkcTlQf5I2tWaWDOQBx4a/kHPufuD+SAqLVWZWGcm+DrFOxxFbdByxxQ/HEcn0h03AXDObaWapwG3A08PaPA18Inz/FuDliRhvFxGRkY3acw+Pof8F8DyhqZAPOud2mtm9QKVz7mngx8DPzGw/oR77beNZtIiInFpE89ydc2uANcM+ds+Q+z3AR6JbWsyK62GlIXQcsUXHEVvi/jg8289dRETGj5Ycioj4kML9JMzsWjPba2b7zexvR3g+zcweCz+/wcwqJr7K0UVwHH9lZrvMbLuZvWRmEU2zmmijHceQdreYmTOzmJzpEMlxmNmfhL8nO83s4YmuMRIR/FxNN7NXzGxL+Gfrei/qPBUze9DMms2s6iTPm5l9N3yM283s7Imu8Yw453QbdiN04vhdYBaQCmwDFg1r8zngB+H7twGPeV33aR7HZUBm+P5n4/U4wu1ygNeA9cBKr+s+ze/HXGALMCn8eIrXdZ/mcdwPfDZ8fxFwyOu6RziOS4CzgaqTPH89sJbQOp7zgQ1e1zyWm3ruI/v9lgvOuT7gvS0XhroJ+Gn4/uPAFRZ76/ZHPQ7n3CvOua7ww/WE1jHEmki+HwD/CPwbEKuXtY/kOD4NfM85dxzAOdc8wTVGIpLjcEBu+H4e718b4znn3GuMsB5niJuAh1zIeiDfzEonprozp3Af2UhbLpSdrI1zLgi8t+VCLInkOIa6k1BPJdaMehxmtgKY5px7diILG6NIvh/zgHlm9qaZrQ/vyBprIjmOrwMfM7NaQjPtPj8xpUXVWN8/MUX7uY8salsueCziGs3sY8BK4NJxrej0nPI4zCwA/BfwyYkq6DRF8v1IJjQ0s5rQX1Gvm9kS51zrONc2FpEcx0eBnzjn/tPMLiC0DmaJcy6yDVhiQzy8x09KPfeRjWXLBU615YLHIjkOzOxK4O+BG51zsXiV4NGOIwdYAqwzs0OExkefjsGTqpH+XD3lnOt3zh0E9hIK+1gSyXHcCfwCwDn3FpBOaL+WeBLR+ydWKdxH5pctF0Y9jvBwxg8JBXssju/CKMfhnGtzzhU65yqccxWEzh3c6Jyr9Kbck4rk5+pJQie5MbNCQsM0Bya0ytFFchzVwBUAZraQULi3TGiVZ+5p4I7wrJnzgTbnXIPXRUXM6zO6sXojdKZ8H6FZAX8f/ti9hEIDQj+svwT2AxuBWV7XfJrH8VugCdgavj3tdc2ncxzD2q4jBmfLRPj9MOBbhK6XsAO4zeuaT/M4FgFvEppJsxW42uuaRziGR4AGoJ9QL/1O4DPAZ4Z8L74XPsYdsfozdbKbVqiKiPiQhmVERHxI4S4i4kMKdxERH1K4i4j4kMJdRMSHFO4iIj6kcBcR8SGFu4iID/1/eWa7Ekbo6esAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25628e22080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hum直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.hum.values,bins=60,kde=True)\n",
    "#看到hum的分布很接近高斯分布，但有离群点，将其除去\n",
    "data = data[data.hum>0.3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25628e22ac8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25628e304e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#windspeed\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.windspeed.values,bins=60,kde=True)\n",
    "#windspeed也基本满足高斯分布，但有些左倾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2562906e5f8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2562893b9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#个人认为分类特征的分布并不能体现与cnt的相关性，于是不对分类特征的分布进行探索\n",
    "#探索两两特征之间的相关性\n",
    "data_corr = data.corr().abs()\n",
    "plt.subplots(figsize=(12,9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "sns.heatmap(data_corr,mask=data_corr<1,cbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察热图后可以看出，temp和atemp与cnt相关性基本相等，进一步证明可以删掉其中一个。\n",
    "虽然holiday,weekday等特征与cnt相关性不高，但出于常识判断，不敢轻易删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25628fdda58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data.drop('temp',axis=1)\n",
    "\n",
    "#按照题目要求，将测试数据与训练数据分离\n",
    "train = data[data.yr==0].drop('yr',axis=1)\n",
    "test = data[data.yr==1].drop('yr',axis=1)\n",
    "\n",
    "#观察训练数据和测试数据的散点图分布\n",
    "plt.scatter(range(train.shape[0]),train.cnt.values)\n",
    "plt.scatter(range(test.shape[0]),test.cnt.values)\n",
    "\n",
    "#这一步是根据第一次实验结果得来，通过观察最后残差的直方图，发现有些左倾，预计是由于2012年整体cnt值比2011年高，所以我选择筛选一下数据，使得两年的数据差距尽量缩小\n",
    "test = test[test.cnt<7800]\n",
    "train=train[train.cnt>500]\n",
    "\n",
    "\n",
    "\n",
    "x_train = train.drop('cnt',axis=1)\n",
    "x_test = test.drop('cnt',axis=1)\n",
    "y_train = train['cnt']\n",
    "y_test = test['cnt']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#分离出数值型数据，并对数值型数据做归一化\n",
    "x_train_type = x_train.drop(['hum','atemp','windspeed'],axis=1)\n",
    "x_train_num = x_train[['hum','atemp','windspeed']]\n",
    "x_test_type =  x_test.drop(['hum','atemp','windspeed'],axis=1)\n",
    "x_test_num = x_test[['hum','atemp','windspeed']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对数值型数据做标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_x_train = StandardScaler()\n",
    "ss_x_test = StandardScaler()\n",
    "x_train_num = ss_x_train.fit_transform(x_train_num)\n",
    "x_test_num = ss_x_test.fit_transform(x_test_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对类别型特征做独热编码\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "enc_train = OneHotEncoder()\n",
    "enc_test = OneHotEncoder()\n",
    "x_train_type = enc_train.fit_transform(x_train_type).toarray()\n",
    "x_test_type = enc_test.fit_transform(x_test_type).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#还原特征矩阵\n",
    "x_train = np.concatenate((x_train_type,x_train_num),axis=1)\n",
    "x_test = np.concatenate((x_test_type,x_test_num),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  \"\"\"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#对cnt做标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_y_train = StandardScaler()\n",
    "ss_y_test = StandardScaler()\n",
    "y_train = ss_y_train.fit_transform(y_train.reshape(-1,1))\n",
    "y_test = ss_y_test.fit_transform(y_test.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8378494453378211\n",
      "0.7182180405688646\n",
      "[[-0.45666398 -0.09165963  0.16772702  0.3805966  -0.44644373 -0.34513678\n",
      "  -0.24345497  0.05914733  0.5928401   0.37848479 -0.1524426  -0.03098906\n",
      "   0.30758143  0.16330577 -0.1260529  -0.15683938  0.09509006 -0.09509006\n",
      "  -0.02284038 -0.02619462 -0.0009619  -0.0271174  -0.01789599  0.0117557\n",
      "   0.0832546  -0.03467584  0.03467584  0.40306021  0.21569554 -0.61875575\n",
      "  -0.16610016  0.45743151 -0.10699342]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "alphas = [0.01,0.1,1,10]\n",
    "lr = RidgeCV(alphas=alphas,store_cv_values=True)\n",
    "lr.fit(x_train,y_train)\n",
    "\n",
    "y_train_pred = lr.predict(x_train)\n",
    "y_test_pred = lr.predict(x_test)\n",
    "\n",
    "print(r2_score(y_train,y_train_pred))\n",
    "print(r2_score(y_test,y_test_pred))\n",
    "\n",
    "print(lr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f,ax = plt.subplots(figsize=(7,5))\n",
    "f.tight_layout()\n",
    "ax.hist(y_test-y_test_pred,bins=40)\n",
    "\n",
    "plt.figure(figsize=(4,3))\n",
    "plt.scatter(y_test,y_test_pred)\n",
    "plt.plot([-3,3],[-3,3],'--k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察结果，基本符合高斯分布，但仍有些点相差0.2左右，目前没有找到好的解决办法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(lr.alpha_)\n",
    "mse_mean = np.mean(lr.cv_values_,axis=0)\n",
    "plt.plot(np.log10(alphas),mse_mean.reshape(len(alphas),1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，alpha在0处有一个明显的转折点，因此，基本可以确定岭回归最佳正则参数应该是1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8153083130167262\n",
      "0.7304877478546746\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "#Lasso回归\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "alphas = [0.01,0.1,1,10,100]\n",
    "ls =LassoCV(alphas=alphas)\n",
    "ls.fit(x_train,y_train)\n",
    "\n",
    "y_train_pred = ls.predict(x_train)\n",
    "y_test_pred = ls.predict(x_test)\n",
    "\n",
    "print(r2_score(y_train,y_train_pred))\n",
    "print(r2_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，在这个例子中，Lasso回归的效果要好于岭回归；在岭回归中，没有对训练集的过拟合起到好的作用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.01\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x256297ce9b0>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2562944d6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(ls.alpha_)\n",
    "mse_mean = np.mean(ls.mse_path_,axis=1)\n",
    "plt.plot(np.log10(ls.alphas_),mse_mean)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但从图中可以看出，alpha在-2.0处仍在下降，于是对参数范围进行缩小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8384149701288246\n",
      "0.7124261538442098\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "alphas = [0.00001,0.0001,0.001]\n",
    "ls =LassoCV(alphas=alphas)\n",
    "ls.fit(x_train,y_train)\n",
    "\n",
    "y_train_pred = ls.predict(x_train)\n",
    "y_test_pred = ls.predict(x_test)\n",
    "\n",
    "print(r2_score(y_train,y_train_pred))\n",
    "print(r2_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0001\n",
      "[-5.93149470e-01 -3.02952798e-01  3.30043846e-06  2.53650144e-01\n",
      " -5.38394915e-01 -4.23687532e-01 -2.81008698e-01  1.00218193e-01\n",
      "  6.67542209e-01  4.53507611e-01 -1.09518841e-01  9.80718817e-04\n",
      "  3.28137005e-01  1.27855888e-01 -1.79705232e-01 -2.21086986e-01\n",
      "  2.64803641e-01 -5.20929575e-17 -7.72337867e-02 -5.41797473e-03\n",
      "  1.94575469e-02 -4.66163511e-03 -0.00000000e+00  2.49436582e-02\n",
      "  2.94354929e-02 -0.00000000e+00  0.00000000e+00  1.94096249e-01\n",
      " -0.00000000e+00 -8.83241999e-01 -1.61519347e-01  4.25329783e-01\n",
      " -1.03508923e-01]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x256294cbc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(ls.alpha_)\n",
    "mse_mean = np.mean(ls.mse_path_,axis=1)\n",
    "plt.plot(np.log10(ls.alphas_),mse_mean)\n",
    "print(ls.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "找到最佳参数为10^0.00001"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
